Clarity on hardware specifications for AI image generation models, particularly VRAM requirements and model parameter partitioning.

Users of the Qwen-Image-2.1 model demand clear hardware specifications, especially regarding VRAM and the scope of the '7b' parameter count. The current lack of clarity creates significant developer friction, evidenced by V100 compatibility concerns. Developer efforts to optimize for lower-end hardware (2G VRAM via ncnn) acknowledge performance trade-offs. This highlights a critical market challenge for AI model providers: balancing power with accessibility. Without explicit hardware guidance and optimized deployment options, adoption will be limited to users with high-end infrastructure, hindering broader market penetration. Clear documentation and diverse deployment targets are essential for expanding the user base and reducing support overhead.